| gamlss.lasso-package {gamlss.lasso} | R Documentation |
Extra Lasso-Type Additive Terms for GAMLSS
Description
Interface for extra high-dimensional smooth functions for Generalized Additive Models for Location Scale and Shape (GAMLSS) including (adaptive) lasso, ridge, elastic net and least angle regression.
Details
The DESCRIPTION file:
| Package: | gamlss.lasso |
| Description: | Interface for extra high-dimensional smooth functions for Generalized Additive Models for Location Scale and Shape (GAMLSS) including (adaptive) lasso, ridge, elastic net and least angle regression. |
| Title: | Extra Lasso-Type Additive Terms for GAMLSS |
| LazyLoad: | yes |
| Version: | 1.0-1 |
| Date: | 2021-05-01 |
| Depends: | R (>= 2.15.0), gamlss (>= 2.4.0), glmnet, lars, Matrix |
| Suggests: | lattice |
| Authors@R: | c(person("Florian", "Ziel", role = c("aut", "cre"), email = "florian.ziel@uni-due.de"), person("Peru", "Muniain", role = "aut"), person("Mikis", "Stasinopoulos", role = "ctb")) |
| Maintainer: | Florian Ziel <florian.ziel@uni-due.de> |
| License: | GPL-2 | GPL-3 |
| URL: | https://www.gamlss.com/ |
| NeedsCompilation: | no |
| Packaged: | 2021-04-01 06:51:36 UTC; florian |
| Repository: | CRAN |
| Date/Publication: | 2021-04-01 06:55:55 UTC |
| Author: | Florian Ziel [aut, cre], Peru Muniain [aut], Mikis Stasinopoulos [ctb] |
Index of help topics:
gamlss.gnet Support for Function gnet() gamlss.lasso-package Extra Lasso-Type Additive Terms for GAMLSS gamlss.lrs Support for Function lrs() gnet (Adaptive) elastic net in GAMLSS lrs Least angle regression and lasso in GAMLSS
Author(s)
NA
Maintainer: Florian Ziel <florian.ziel@uni-due.de>
References
R Rigby, R. A. and Stasinopoulos D. M. (2005). Generalized additive models for location, scale and shape, (with discussion), Appl. Statist., 54, part 3, pp 507-554.
Rigby R.A., Stasinopoulos D. M., Heller G., and De Bastiani F., (2019) Distributions for Modeling Location, Scale and Shape: Using GAMLSS in R, Chapman and Hall/CRC.
Stasinopoulos D. M. Rigby R.A. (2007) Generalized additive models for location scale and shape (GAMLSS) in R. Journal of Statistical Software, Vol. 23, Issue 7, Dec 2007, https://www.jstatsoft.org/v23/i07.
Stasinopoulos D. M., Rigby R.A., Heller G., Voudouris V., and De Bastiani F., (2017) Flexible Regression and Smoothing: Using GAMLSS in R, Chapman and Hall/CRC.
(see also https://www.gamlss.com/).
Efron, B., Hastie, T., Johnstone, I., & Tibshirani, R. (2004). Least angle regression. Annals of statistics, 32(2), 407-499.
Friedman, J., Hastie, T., & Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of statistical software, 33(1), 1.
See Also
gamlss, gamlss.family, gamlss.add
Examples
# Contructing the data
library(gamlss.lasso)
set.seed(123)
n<- 500
d<- 50
X<- matrix(rnorm(n*d), n,d)
BETA<- cbind( "mu"=rbinom(d,1,.1), "sigma"= rbinom(d,1,.1)*.3)
ysd<- exp(1 + tcrossprod( BETA[,2],X))
data<- cbind(y=as.numeric(rnorm(n, sd=ysd))+t(tcrossprod( BETA[,1],X)), as.data.frame(X))
# Estimating the model with gnet default setting
mod <- gamlss(y~gnet(x.vars=names(data)[-1] ),
sigma.fo=~gnet(x.vars=names(data)[-1]), data=data, family=NO,
i.control = glim.control(cyc=1, bf.cyc=1))
# Estimated paramters are available at
rbind(true=BETA[,1],estimate=tail(getSmo(mod, "mu") ,1)[[1]]$beta )## beta for mu
rbind(true=BETA[,2],estimate=tail(getSmo(mod, "sigma") ,1)[[1]]$beta )## beta for sigma